arXiv AI July 20, 2026 By Ajay Madhavan Ravichandran, Bilgin Osmandoja, Klemens Budde, Klaus Netter, Tobias Strapatsas, Aljoscha Burchardt, Sebastian M\"oller, Roland Roller

Large Language Models as Unified Multimodal Learners for Clinical Prediction

Read the original on arXiv AI →

arXiv:2607. 15380v1 Announce Type: cross Abstract: Electronic health records combine free-text clinical narratives with structured measurements such as vital signs, laboratory values, and comorbidities.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Jul 20

LLM4EHR: Aligning Clinical Time Series with Medical Event Sequences via Large Language Models

arXiv:2607. 15447v1 Announce Type: new Abstract: Recent research in clinical machine learning, focusing on outcome predictions in intensive care unit (ICU), has shifted from bespoke supervised models to foundation models, utilising modern representation learning methods.

By Jingteng Li, Alexander Capstick, Louise Rigny, Iona Biggart, Neil J Sebire, Payam Barnaghi

llms rag benchmarks

arXiv AI

Jun 30

Primary ICD Category Prediction using LLM-based Probing

arXiv:2606. 28798v1 Announce Type: new Abstract: Objective: ICD codes are central to reimbursement, research, and population health surveillance, yet automated coding systems often struggle to integrate diagnostic signals from both clinical narratives and structured electronic health record (EHR) variables.

By Chengyuan Liu, Xinyue Zhang, Yao Li, Guanting Chen

llms rag multimodal

arXiv AI

Jun 16

Fusion is not one-size-fits-all: Cross-Modal Representation Alignment for Time-to-Event Modeling

arXiv:2606. 15038v1 Announce Type: new Abstract: Accurate time-to-event (TTE) prediction from multimodal clinical data remains challenging due to modality imbalance and distribution shift.

By Zhemin Zhang, Weijie Chen, David Le, Amara Tariq, Alex Wallace, Matthew Stib, Juan Maria Farina, Chadi Ayoub, Reza Arsanjani, Imon Banerjee

diffusion multimodal safety

arXiv AI

Sep 23

A General Multimodal Probing Framework for Data Fusion and Prediction with Frozen Large Language Models: Evidence from Multimodal EHR Data

arXiv:2606.28798v2 Announce Type: replace Abstract: The main objective of this paper is to propose a general framework for prediction based on different sources of multimodal data in the healthcare d...

By Chengyuan Liu, Xinyue Zhang, Yao Li, Guanting Chen

llms rag fine-tuning multimodal

arXiv Computation and Language

Aug 21

Towards Clinically Faithful Medical Image Captioning via Enhanced Vision-Language Alignment

arXiv:2608. 19825v1 Announce Type: cross Abstract: Medical image captioning is a technique that accelerates early-stage diagnostic workflows and enhances the interpretability of medical diagnostic AI systems.

By Yunseo Lee, Hyun Jun Kim, Heeseung Shin, Changwon Lim

llms rag multimodal safety data-engineering

arXiv Machine Learning

Sep 15

Multi-Modal Tumor Survival Prediction via Graph-Guided Mixture of Experts

arXiv:2609.14072v1 Announce Type: new Abstract: Large Language Models (LLMs) have displayed impressive capabilities in handling tasks that require few demonstration examples, making them effective fe...

By H Mathavan, H Liu

llms multimodal

One email a morning, machine-written

Related stories

LLM4EHR: Aligning Clinical Time Series with Medical Event Sequences via Large Language Models

Primary ICD Category Prediction using LLM-based Probing

Fusion is not one-size-fits-all: Cross-Modal Representation Alignment for Time-to-Event Modeling

A General Multimodal Probing Framework for Data Fusion and Prediction with Frozen Large Language Models: Evidence from Multimodal EHR Data

Towards Clinically Faithful Medical Image Captioning via Enhanced Vision-Language Alignment

Multi-Modal Tumor Survival Prediction via Graph-Guided Mixture of Experts